SoroushVahidi/scidocs huggingface dataset:
Socializing by Gaming: Revealing Social Relationships in Multiplayer Online Games
(2015)

Multiplayer Online Games (MOGs) like Defense of the Ancients and StarCraft II have attracted hundreds of millions of users who communicate, interact, and socialize with each other through gaming. In MOGs, rich social relationships emerge and can be used to improve gaming services such as match recommendation and game population retention, which are important for the user experience and the commercial value of the companies who run these MOGs. In this work, we focus on understanding social relationships in MOGs. We propose a graph model that is able to capture social relationships of a variety of types and strengths. We apply our model to real-world data collected from three MOGs that contain in total over ten years of behavioral history for millions of players and matches. We compare social relationships in MOGs across different game genres and with regular online social networks like Facebook. Taking match recommendation as an example application of our model, we propose SAMRA, a Socially Aware Match Recommendation Algorithm that takes social relationships into account. We show that our model not only improves the precision of traditional link prediction approaches, but also potentially helps players enjoy games to a higher extent.
Private Collaborative Neural Network Learning
(2017)

Machine learning algorithms, such as neural networks, create better predictive models when having access to larger datasets. In many domains, such as medicine and finance, each institute has only access to limited amounts of data, and creating larger datasets typically requires collaboration. However, there are privacy related constraints on these collaborations for legal, ethical, and competitive reasons. In this work, we present a feasible protocol for learning neural networks in a collaborative way while preserving the privacy of each record. This is achieved by combining Differential Privacy and Secure Multi-Party Computation with Machine Learning.
Supervised Hashing with Deep Neural Networks
(2015)

In this paper, we propose training very deep neural networks (DNNs) for supervised learning of hash codes. Existing methods in this context train relatively “shallow” networks limited by the issues arising in back propagation (e.g. vanishing gradients) as well as computational efficiency. We propose a novel and efficient training algorithm inspired by alternating direction method of multipliers (ADMM) that overcomes some of these limitations. Our method decomposes the training process into independent layer-wise local updates through auxiliary variables. Empirically we observe that our training algorithm always converges and its computational complexity is linearly proportional to the number of edges in the networks. Empirically we manage to train DNNs with 64 hidden layers and 1024 nodes per layer for supervised hashing in about 3 hours using a single GPU. Our proposed very deep supervised hashing (VDSH) method significantly outperforms the state-of-theart on several benchmark datasets.
Treatment of thoracolumbar burst fractures without neurologic deficit by indirect reduction and posterior instrumentation: bisegmental stabilization with monosegmental fusion
(1999)

This study retrospectively reviews 20 sequential patients with thoracolumbar burst fractures without neurologic deficit. All patients were treated by indirect reduction, bisegmental posterior transpedicular instrumentation and monosegmental fusion. Clinical and radiological outcome was analyzed after an average follow-up of 6.4 years. Re-kyphosis of the entire segment including the cephaled disc was significant with loss of the entire postoperative correction over time. This did not influence the generally benign clinical outcome. Compared to its normal height the fused cephalad disc was reduced by 70% and the temporarily spanned caudal disc by 40%. Motion at the temporarily spanned segment could be detected in 11 patients at follow-up, with no relation to the clinical result. Posterior instrumentation of thoracolumbar burst fractures can initially reduce the segmental kyphosis completely. The loss of correction within the fractured vertebral body is small. However, disc space collapse leads to eventual complete loss of segmental reduction. Therefore, posterolateral fusion alone does not prevent disc space collapse. Nevertheless, clinical long-term results are favorable. However, if disc space collapse has to prevented, an interbody disc clearance and fusion is recommended.
Socioeconomic disparities in health: pathways and policies.
(2002)

Socioeconomic status (SES) underlies three major determinants of health: health care, environmental exposure, and health behavior. In addition, chronic stress associated with lower SES may also increase morbidity and mortality. Reducing SES disparities in health will require policy initiatives addressing the components of socioeconomic status (income, education, and occupation) as well as the pathways by which these affect health. Lessons for U.S. policy approaches are taken from the Acheson Commission in England, which was charged with reducing health disparities in that country.
Human wayfinding in information networks
(2012)

Navigating information spaces is an essential part of our everyday lives, and in order to design efficient and user-friendly information systems, it is important to understand how humans navigate and find the information they are looking for. We perform a large-scale study of human wayfinding, in which, given a network of links between the concepts of Wikipedia, people play a game of finding a short path from a given start to a given target concept by following hyperlinks. What distinguishes our setup from other studies of human Web-browsing behavior is that in our case people navigate a graph of connections between concepts, and that the exact goal of the navigation is known ahead of time. We study more than 30,000 goal-directed human search paths and identify strategies people use when navigating information spaces. We find that human wayfinding, while mostly very efficient, differs from shortest paths in characteristic ways. Most subjects navigate through high-degree hubs in the early phase, while their search is guided by content features thereafter. We also observe a trade-off between simplicity and efficiency: conceptually simple solutions are more common but tend to be less efficient than more complex ones. Finally, we consider the task of predicting the target a user is trying to reach. We design a model and an efficient learning algorithm. Such predictive models of human wayfinding can be applied in intelligent browsing interfaces.
Designing and Packaging Wide-Band PAs: Wideband PA and Packaging, History, and Recent Advances: Part 1
(2016)

In current applications such as communication, aerospace and defense, electronic warfare (EW), electromagnetic compatibility (EMC), and sensing, among others, there is an ever-growing demand for more linear power with increasingly greater bandwidth and efficiency. Critical for these applications are the design and packaging of wide-band, high-power amplifiers that are both compact in size and low in cost [1]. Most such applications, including EW, radar, and EMC testers, require high 1-dB compression point (P1dB) power with good linearity across a wide band (multi-octave to decade bandwidth) [2]. In addition to linear power, wide bandwidth is essential for high-data-rate communication and high resolution in radar and active imagers [3]. In modern electronics equipment such as automated vehicles, rapidly increasing complexity imposes strict EMC regulations for human safety and security [4]. This often requires challenging specifications for the power amplifier (PA), such as very high P1dB power [to kilowatt, continuous wave (CW)] across approximately a decade bandwidth with high linearity, reliability, and long life, even for 100% load mismatch [5].
Grid-Connected Photovoltaic Generation System
(2008)

This study addresses a grid-connected photovoltaic (PV) generation system. In order to make the PV generation system more flexible and expandable, the backstage power circuit is composed of a high step-up converter and a pulsewidth-modulation (PWM) inverter. In the dc-dc power conversion, the high step-up converter is introduced to improve the conversion efficiency of conventional boost converters and to allow the parallel operation of low-voltage PV modules. Moreover, an adaptive total sliding-mode control system is designed for the current control of the PWM inverter to maintain the output current with a higher power factor and less variation under load changes. In addition, an adaptive step-perturbation method is proposed to achieve the objective of maximum power point tracking, and an active sun tracking scheme without any light sensors is investigated to make PV plates face the sun directly in order to capture maximum irradiation and enhance system efficiency. Experimental results are given to verify the validity of the high step-up converter, the PWM inverter control, the ASP method, and the active sun tracker for a grid-connected PV generation system.
Development of the Perceived Stress Questionnaire: a new tool for psychosomatic research.
(1993)

A 30-question Perceived Stress Questionnaire (PSQ) was validated, in Italian and English, among 230 subjects. Test-retest reliability was 0.82 for the General (past year or two) PSQ, while monthly Recent (past month) PSQs varied by a mean factor of 1.9 over 6 months; coefficient alpha > 0.9. General and/or Recent PSQ scores were associated with trait anxiety (r = 0.75), Cohen's Perceived Stress Scale (r = 0.73), depression (r = 0.56), self-rated stress (r = 0.56), and stressful life events (p < 0.05). The General PSQ was higher in in-patients than in out-patients (p < 0.05); both forms were correlated with a somatic complaints scale in a non-patient population (r > 0.5), and were higher, among 27 asymptomatic ulcerative colitis patients, in the seven who had rectal inflammation than in those with normal proctoscopy (p = 0.03). Factor analysis yielded seven factors, of which those reflecting interpersonal conflict and tension were significantly associated with health outcomes. The Perceived Stress Questionnaire may be a valuable addition to the armamentarium of psychosomatic researchers.
Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5
(2015)

Fine particulate matter ( $$\hbox {PM}_{2.5}$$ PM 2.5 ) has a considerable impact on human health, the environment and climate change. It is estimated that with better predictions, US$9 billion can be saved over a 10-year period in the USA (State of the science fact sheet air quality. http://www.noaa.gov/factsheets/new , 2012). Therefore, it is crucial to keep developing models and systems that can accurately predict the concentration of major air pollutants. In this paper, our target is to predict $$\hbox {PM}_{2.5}$$ PM 2.5 concentration in Japan using environmental monitoring data obtained from physical sensors with improved accuracy over the currently employed prediction models. To do so, we propose a deep recurrent neural network (DRNN) that is enhanced with a novel pre-training method using auto-encoder especially designed for time series prediction. Additionally, sensors selection is performed within DRNN without harming the accuracy of the predictions by taking advantage of the sparsity found in the network. The numerical experiments show that DRNN with our proposed pre-training method is superior than when using a canonical and a state-of-the-art auto-encoder training method when applied to time series prediction. The experiments confirm that when compared against the $$\hbox {PM}_{2.5}$$ PM 2.5 prediction system VENUS (National Institute for Environmental Studies. Visual Atmospheric Environment Utility System. http://envgis5.nies.go.jp/osenyosoku/ , 2014), our technique improves the accuracy of $$\hbox {PM}_{2.5}$$ PM 2.5 concentration level predictions that are being reported in Japan.
Feature selection in text classification
(2016)

In recent years, text classification have been widely used. Dimension of text data has increased more and more. Working of almost all classification algorithms is directly related to dimension. In high dimension data set, working of classification algorithms both takes time and occurs over fitting problem. So feature selection is crucial for machine learning techniques. In this study, frequently used feature selection metrics Chi Square (CHI), Information Gain (IG) and Odds Ratio (OR) have been applied. At the same time the method Relevancy Frequency (RF) proposed as term weighting method has been used as feature selection method in this study. It is used for tf.idf term as weighting method, Sequential Minimal Optimization (SMO) and Naive Bayes (NB) in the classification algorithm. Experimental results show that RF gives successful results.
Design of a Multi-Sensor Cooperation Travel Environment Perception System for Autonomous Vehicle
(2012)

This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic sign recognition. Multiple single scan lasers are integrated to detect the road curb based on Z-variance method. Vision based lane detection is realized by two scans method combining with image model. Haar-like feature based method is applied for traffic sign detection and SURF matching method is used for sign classification. The results of experiments validate the effectiveness of the proposed algorithms and the whole system.
3D laser scan classification using web data and domain adaptation
(2009)

Over the last years, object recognition has become a more and more active field of research in robotics. An important problem in object recognition is the need for sufficient labeled training data to learn good classifiers. In this paper we show how to significantly reduce the need for manually labeled training data by leveraging data sets available on the World Wide Web. Specifically, we show how to use objects from Google’s 3D Warehouse to train classifiers for 3D laser scans collected by a robot navigating through urban environments. In order to deal with the different characteristics of the web data and the real robot data, we additionally use a small set of labeled 3D laser scans and perform domain adaptation. Our experiments demonstrate that additional data taken from the 3D Warehouse along with our domain adaptation greatly improves the classification accuracy on real laser scans.
Detecting human activities in retail surveillance using hierarchical finite state machine
(2011)

Cashiers in retail stores usually exhibit certain repetitive and periodic activities when processing items. Detecting such activities plays a key role in most retail fraud detection systems. In this paper, we propose a highly efficient, effective and robust vision technique to detect checkout-related primitive activities, based on a hierarchical finite state machine (FSM). Our deterministic approach uses visual features and prior spatial constraints on the hand motion to capture particular motion patterns performed in primitive activities. We also apply our approach to the problem of retail fraud detection. Experimental results on a large set of video data captured from retail stores show that our approach, while much simpler and faster, achieves significantly better results than state-of-the-art machine learning-based techniques both in detecting checkout-related activities and in detecting checkout-related fraudulent incidents.
COMPARING PROGRAMMER PRODUCTIVITY IN OPENACC AND CUDA: AN EMPIRICAL INVESTIGATION
(2016)

OpenACC has been touted as a "high productivity" API designed to make GPGPU programming accessible to scientific programmers, but to date, no studies have attempted to verify this quantitatively. In this paper, we conduct an empirical investigation of program productivity comparisons between OpenACC and CUDA in the programming time, the execution time and the analysis of independence of OpenACC model in high performance problems. Our results show that, for our programs and our subject pool, this claim is true. We created two assignments called Machine Problem 3(MP3) and Machine Problem 4(MP4) in the classroom environment and instrumented the WebCode website developed by ourselves to record details of students’ coding process. Three hypotheses were supported by the statistical data: for the same parallelizable problem, (1) the OpenACC programming time is at least 37% shorter than CUDA; (2) the CUDA running speed is 9x faster than OpenACC; (3) the OpenACC development work is not significantly affected by previous CUDA experience
PROFITING FROM ARBITRAGE AND ODDS BIASES OF THE EUROPEAN FOOTBALL GAMBLING MARKET
(2015)

A gambling market is usually described as being inefficient if there are one or more betting strategies that generate profit, at a consistent rate, as a consequence of exploiting market flaws. This paper examines the online European football gambling market based on 14 European football leagues over a period of seven years, from season 2005/06 to 2011/12 inclusive, and takes into consideration the odds provided by numerous bookmaking firms. Contrary to common misconceptions, we demonstrate that the accuracy of bookmakers' odds has not improved over this period. More importantly, our results question market efficiency by demonstrating high profitability on the basis of consistent odds biases and numerous arbitrage opportunities.
Low-cost, real-time obstacle avoidance for mobile robots
(2017)

The goal of this project1 is to advance the field of automation and robotics by utilizing recently-released, low-cost sensors and microprocessors to develop a mechanism that provides depth-perception and autonomous obstacle avoidance in a plug-and-play fashion. We describe the essential hardware components that can enable such a low-cost solution and an algorithm to avoid static obstacles present in the environment. The mechanism utilizes a novel single-point LIDAR module that affords more robustness and invariance than popular approaches, such as Neural Networks and Stereo. When this hardware is coupled with the proposed efficient obstacle avoidance algorithm, this mechanism is able to accurately represent environments through point clouds and construct obstacle-free paths to a destination, in a small timeframe. A prototype mechanism has been installed on a quadcopter for visualization on how actual implementation may take place2. We describe experimental results based on this prototype.
Joint tracking and segmentation of multiple targets
(2015)

Tracking-by-detection has proven to be the most successful strategy to address the task of tracking multiple targets in unconstrained scenarios [e.g. 40, 53, 55]. Traditionally, a set of sparse detections, generated in a preprocessing step, serves as input to a high-level tracker whose goal is to correctly associate these “dots” over time. An obvious short-coming of this approach is that most information available in image sequences is simply ignored by thresholding weak detection responses and applying non-maximum suppression. We propose a multi-target tracker that exploits low level image information and associates every (super)-pixel to a specific target or classifies it as background. As a result, we obtain a video segmentation in addition to the classical bounding-box representation in unconstrained, real-world videos. Our method shows encouraging results on many standard benchmark sequences and significantly outperforms state-of-the-art tracking-by-detection approaches in crowded scenes with long-term partial occlusions.
Parallel Formulations of Decision-Tree Classification Algorithms
(1999)

Classification decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, etc. Highly parallel algorithms for constructing classification decision trees are desirable for dealing with large data sets in reasonable amount of time. Algorithms for building classification decision trees have a natural concurrency, but are difficult to parallelize due to the inherent dynamic nature of the computation. In this paper, we present parallel formulations of classification decision tree learning algorithm based on induction. We describe two basic parallel formulations. One is based on Synchronous Tree Construction Approach and the other is based on Partitioned Tree Construction Approach. We discuss the advantages and disadvantages of using these methods and propose a hybrid method that employs the good features of these methods. We also provide the analysis of the cost of computation and communication of the proposed hybrid method. Moreover, experimental results on an IBM SP-2 demonstrate excellent speedups and scalability.
Indian Sign Language gesture classification as single or double handed gestures
(2015)

The development of a sign language recognition system can have a great impact on the daily lives of humans with hearing disabilities. Recognizing gestures from the Indian Sign Language (ISL) with a camera can be difficult due to complexity of various gestures. The motivation behind the paper is to develop an approach to successfully classify gestures in the ISL under ambiguous conditions from static images. A novel approach involving the decomposition of gestures into single handed or double handed gesture has been presented in this paper. Classifying gesture into these subcategories simplifies the process of gesture recognition in the ISL due to presence of lesser number of gestures in each subcategory. Various approaches making use of Histogram of Gradients (HOG) features and geometric descriptors using KNN and SVM classifiers were tried on a dataset consisting of images of all 26 English alphabets present in the ISL under variable background. HOG features when classified with Support Vector Machine were found to be the most efficient approach resulting in an accuracy of 94.23%.
Estimation of the Mathematical Parameters of Double-Exponential Pulses Using the Nelder–Mead Algorithm
(2010)

Transient pulses for electromagnetic compatibility problems, such as the high-altitude electromagnetic pulse and ultrawideband pulses, are often described by a double-exponential pulse. Such a pulse shape is specified physically by the three characteristic parameters rise time tr, pulsewidth tfwhm (full-width at half-maximum), and maximum amplitude Emax. The mathematical description is a double-exponential function with the parameters α, β, and E0. In practice, it is often necessary to transform the two groups of parameters into each other. This paper shows a novel relationship between the physical parameters tr and tfwhm on the one hand and the mathematical parameters α and β on the other. It is shown that the least-squares method in combination with the Nelder-Mead simplex algorithm is appropriate to determine an approximate closed-form formula between these parameters. Therefore, the extensive analysis of double-exponential pulses is possible in a considerably shorter computation time. The overall approximation error is less than 3.8%.